TY - GEN
T1 - Peeling back the layers
T2 - 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011
AU - Huang, Ruihong
AU - Riloff, Ellen
PY - 2011
Y1 - 2011
N2 - The goal of our research is to improve event extraction by learning to identify secondary role filler contexts in the absence of event keywords. We propose a multilayered event extraction architecture that progressively "zooms in" on relevant information. Our extraction model includes a document genre classifier to recognize event narratives, two types of sentence classifiers, and noun phrase classifiers to extract role fillers. These modules are organized as a pipeline to gradually zero in on event-related information. We present results on the MUC-4 event extraction data set and show that this model performs better than previous systems.
AB - The goal of our research is to improve event extraction by learning to identify secondary role filler contexts in the absence of event keywords. We propose a multilayered event extraction architecture that progressively "zooms in" on relevant information. Our extraction model includes a document genre classifier to recognize event narratives, two types of sentence classifiers, and noun phrase classifiers to extract role fillers. These modules are organized as a pipeline to gradually zero in on event-related information. We present results on the MUC-4 event extraction data set and show that this model performs better than previous systems.
UR - https://www.scopus.com/pages/publications/84859053621
UR - https://www.scopus.com/pages/publications/84859053621#tab=citedBy
M3 - Conference contribution
AN - SCOPUS:84859053621
SN - 9781932432879
T3 - ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
SP - 1137
EP - 1147
BT - ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics
Y2 - 19 June 2011 through 24 June 2011
ER -